Overview

Dataset statistics

Number of variables14
Number of observations8132
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory794.3 KiB
Average record size in memory100.0 B

Variable types

NUM11
CAT3

Warnings

fatality_count is highly skewed (γ1 = 62.73472417) Skewed
df_index has unique values Unique
fatality_count has 5956 (73.2%) zeros Zeros
admin_division_population has 946 (11.6%) zeros Zeros
landslide_category_code has 167 (2.1%) zeros Zeros
landslide_size_code has 530 (6.5%) zeros Zeros

Reproduction

Analysis started2020-12-01 12:37:42.900557
Analysis finished2020-12-01 12:37:59.840250
Duration16.94 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct8132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5087.675603
Minimum0
Maximum10012
Zeros1
Zeros (%)< 0.1%
Memory size63.5 KiB
2020-12-01T12:37:59.939091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile800.55
Q12688.75
median5092.5
Q37483.25
95-th percentile9388.45
Maximum10012
Range10012
Interquartile range (IQR)4794.5

Descriptive statistics

Standard deviation2771.544186
Coefficient of variation (CV)0.5447564668
Kurtosis-1.19744858
Mean5087.675603
Median Absolute Deviation (MAD)2398
Skewness-0.01055601702
Sum41372978
Variance7681457.173
MonotocityStrictly increasing
2020-12-01T12:38:00.068481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
74971< 0.1%
 
13301< 0.1%
 
33791< 0.1%
 
95261< 0.1%
 
54321< 0.1%
 
74811< 0.1%
 
13381< 0.1%
 
95341< 0.1%
 
54401< 0.1%
 
Other values (8122)812299.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
100121< 0.1%
 
99951< 0.1%
 
99321< 0.1%
 
99101< 0.1%
 
98811< 0.1%
 
Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
landslide
5616 
mudslide
1588 
rock_fall
 
456
complex
 
167
debris_flow
 
155
Other values (8)
 
150
ValueCountFrequency (%) 
landslide561669.1%
 
mudslide158819.5%
 
rock_fall4565.6%
 
complex1672.1%
 
debris_flow1551.9%
 
other650.8%
 
unknown360.4%
 
riverbank_collapse240.3%
 
snow_avalanche70.1%
 
translational_slide60.1%
 
Other values (3)120.1%
 
2020-12-01T12:38:00.184165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-01T12:38:00.291792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length9
Mean length8.795130349
Min length5
Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
downpour
3880 
unknown
1486 
rain
1327 
continuous_rain
516 
tropical_cyclone
451 
Other values (11)
472 
ValueCountFrequency (%) 
downpour388047.7%
 
unknown148618.3%
 
rain132716.3%
 
continuous_rain5166.3%
 
tropical_cyclone4515.5%
 
monsoon1071.3%
 
earthquake740.9%
 
mining670.8%
 
snowfall_snowmelt640.8%
 
construction500.6%
 
Other values (6)1101.4%
 
2020-12-01T12:38:00.406167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-01T12:38:00.519180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length8
Mean length8.179291687
Min length4

landslide_size
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.5 KiB
medium
4969 
small
1758 
unknown
806 
large
530 
very_large
 
69
ValueCountFrequency (%) 
medium496961.1%
 
small175821.6%
 
unknown8069.9%
 
large5306.5%
 
very_large690.8%
 
2020-12-01T12:38:00.626473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-01T12:38:00.707299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:38:00.893183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length6
Mean length5.851697
Min length5

fatality_count
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct99
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.551155927
Minimum0
Maximum5000
Zeros5956
Zeros (%)73.2%
Memory size63.5 KiB
2020-12-01T12:38:01.003247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10
Maximum5000
Range5000
Interquartile range (IQR)1

Descriptive statistics

Standard deviation64.98300935
Coefficient of variation (CV)18.29911462
Kurtosis4499.642614
Mean3.551155927
Median Absolute Deviation (MAD)0
Skewness62.73472417
Sum28878
Variance4222.791504
MonotocityNot monotonic
2020-12-01T12:38:01.133079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0595673.2%
 
15326.5%
 
23554.4%
 
32683.3%
 
41652.0%
 
51551.9%
 
61111.4%
 
7851.0%
 
8520.6%
 
10490.6%
 
Other values (89)4045.0%
 
ValueCountFrequency (%) 
0595673.2%
 
15326.5%
 
23554.4%
 
32683.3%
 
41652.0%
 
ValueCountFrequency (%) 
50001< 0.1%
 
21001< 0.1%
 
17651< 0.1%
 
4911< 0.1%
 
4301< 0.1%
 

admin_division_population
Real number (ℝ≥0)

ZEROS

Distinct3150
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159893.2088
Minimum0
Maximum12691836
Zeros946
Zeros (%)11.6%
Memory size63.5 KiB
2020-12-01T12:38:01.255166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11895
median7107.5
Q332685.5
95-th percentile522081
Maximum12691836
Range12691836
Interquartile range (IQR)30790.5

Descriptive statistics

Standard deviation842613.5674
Coefficient of variation (CV)5.269852133
Kurtosis111.1887498
Mean159893.2088
Median Absolute Deviation (MAD)7107.5
Skewness9.727766598
Sum1300251574
Variance7.09997624e+11
MonotocityNot monotonic
2020-12-01T12:38:01.389269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
094611.6%
 
2033480.6%
 
456456460.6%
 
6023699450.6%
 
1035410.5%
 
8466380.5%
 
2000350.4%
 
4154350.4%
 
92113340.4%
 
1795340.4%
 
Other values (3140)683084.0%
 
ValueCountFrequency (%) 
094611.6%
 
581< 0.1%
 
741< 0.1%
 
811< 0.1%
 
1401< 0.1%
 
ValueCountFrequency (%) 
1269183680.1%
 
122941932< 0.1%
 
116242192< 0.1%
 
110714241< 0.1%
 
103583811< 0.1%
 

gazeteer_distance
Real number (ℝ≥0)

Distinct8036
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.9581559
Minimum3e-05
Maximum199.44893
Zeros0
Zeros (%)0.0%
Memory size63.5 KiB
2020-12-01T12:38:01.517911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3e-05
5-th percentile0.507763
Q12.3975475
median6.351405
Q315.9228125
95-th percentile40.987829
Maximum199.44893
Range199.4489
Interquartile range (IQR)13.525265

Descriptive statistics

Standard deviation15.54313425
Coefficient of variation (CV)1.299793578
Kurtosis21.11072739
Mean11.9581559
Median Absolute Deviation (MAD)4.93768
Skewness3.475495048
Sum97243.7238
Variance241.5890222
MonotocityNot monotonic
2020-12-01T12:38:01.647086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.753024< 0.1%
 
20.356514< 0.1%
 
2.54864< 0.1%
 
31.546293< 0.1%
 
23.180653< 0.1%
 
1.065433< 0.1%
 
6.038853< 0.1%
 
0.216573< 0.1%
 
69.961922< 0.1%
 
27.206272< 0.1%
 
Other values (8026)810199.6%
 
ValueCountFrequency (%) 
3e-051< 0.1%
 
4e-051< 0.1%
 
0.000821< 0.1%
 
0.001781< 0.1%
 
0.002741< 0.1%
 
ValueCountFrequency (%) 
199.448931< 0.1%
 
197.804231< 0.1%
 
178.237061< 0.1%
 
176.022021< 0.1%
 
168.320981< 0.1%
 

longitude
Real number (ℝ)

Distinct7914
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1709164339
Minimum-170.7998
Maximum179.4104
Zeros0
Zeros (%)0.0%
Memory size63.5 KiB
2020-12-01T12:38:01.772322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-170.7998
5-th percentile-123.8193
Q1-108.037475
median8.38765
Q393.77895
95-th percentile125.54559
Maximum179.4104
Range350.2102
Interquartile range (IQR)201.816425

Descriptive statistics

Standard deviation100.8266457
Coefficient of variation (CV)589.9177946
Kurtosis-1.681286494
Mean0.1709164339
Median Absolute Deviation (MAD)95.50715
Skewness-0.01377882839
Sum1389.89244
Variance10166.01249
MonotocityNot monotonic
2020-12-01T12:38:01.898091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-123.848160.1%
 
-123.892450.1%
 
-123.577750.1%
 
-122.61694< 0.1%
 
-123.58274< 0.1%
 
-122.13554< 0.1%
 
-76.33024< 0.1%
 
-122.86974< 0.1%
 
-121.87844< 0.1%
 
-122.87713< 0.1%
 
Other values (7904)808999.5%
 
ValueCountFrequency (%) 
-170.79981< 0.1%
 
-170.67771< 0.1%
 
-170.6511< 0.1%
 
-170.58251< 0.1%
 
-159.56821< 0.1%
 
ValueCountFrequency (%) 
179.41041< 0.1%
 
179.39971< 0.1%
 
179.04211< 0.1%
 
178.70811< 0.1%
 
178.53581< 0.1%
 

latitude
Real number (ℝ)

Distinct7828
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.76034564
Minimum-46.7748
Maximum72.6275
Zeros0
Zeros (%)0.0%
Memory size63.5 KiB
2020-12-01T12:38:02.022233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-46.7748
5-th percentile-18.93215
Q113.7851
median30.382
Q341.882675
95-th percentile48.367235
Maximum72.6275
Range119.4023
Interquartile range (IQR)28.097575

Descriptive statistics

Standard deviation20.58012598
Coefficient of variation (CV)0.7989072144
Kurtosis1.003384276
Mean25.76034564
Median Absolute Deviation (MAD)13.43385
Skewness-1.099977958
Sum209483.1307
Variance423.5415852
MonotocityNot monotonic
2020-12-01T12:38:02.147497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
45.563590.1%
 
44.15860.1%
 
45.559850.1%
 
43.304750.1%
 
44.11654< 0.1%
 
46.11854< 0.1%
 
44.39984< 0.1%
 
43.02664< 0.1%
 
45.53174< 0.1%
 
42.67983< 0.1%
 
Other values (7818)808499.4%
 
ValueCountFrequency (%) 
-46.77481< 0.1%
 
-45.90341< 0.1%
 
-45.87671< 0.1%
 
-45.87271< 0.1%
 
-45.86181< 0.1%
 
ValueCountFrequency (%) 
72.62751< 0.1%
 
65.97941< 0.1%
 
65.02161< 0.1%
 
65.01671< 0.1%
 
64.41121< 0.1%
 

year
Real number (ℝ≥0)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.351451
Minimum1988
Maximum2016
Zeros0
Zeros (%)0.0%
Memory size63.5 KiB
2020-12-01T12:38:02.254146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1988
5-th percentile2008
Q12010
median2013
Q32015
95-th percentile2016
Maximum2016
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.604820203
Coefficient of variation (CV)0.001294416143
Kurtosis3.345513839
Mean2012.351451
Median Absolute Deviation (MAD)2
Skewness-0.8362498618
Sum16364442
Variance6.785088289
MonotocityNot monotonic
2020-12-01T12:38:02.357801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
2010149218.3%
 
2015132016.2%
 
2011128315.8%
 
2014103212.7%
 
201688710.9%
 
201383010.2%
 
20125096.3%
 
20093504.3%
 
20082212.7%
 
20071632.0%
 
Other values (10)450.6%
 
ValueCountFrequency (%) 
19881< 0.1%
 
19931< 0.1%
 
19951< 0.1%
 
19962< 0.1%
 
1997100.1%
 
ValueCountFrequency (%) 
201688710.9%
 
2015132016.2%
 
2014103212.7%
 
201383010.2%
 
20125096.3%
 

month
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.538366945
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size63.5 KiB
2020-12-01T12:38:02.458153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.359584382
Coefficient of variation (CV)0.5138262215
Kurtosis-1.083906236
Mean6.538366945
Median Absolute Deviation (MAD)3
Skewness-0.04238731914
Sum53170
Variance11.28680722
MonotocityNot monotonic
2020-12-01T12:38:02.640129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
799812.3%
 
885110.5%
 
37469.2%
 
127289.0%
 
67058.7%
 
16948.5%
 
96508.0%
 
106227.6%
 
45697.0%
 
25436.7%
 
Other values (2)102612.6%
 
ValueCountFrequency (%) 
16948.5%
 
25436.7%
 
37469.2%
 
45697.0%
 
55406.6%
 
ValueCountFrequency (%) 
127289.0%
 
114866.0%
 
106227.6%
 
96508.0%
 
885110.5%
 

landslide_category_code
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.320585342
Minimum0
Maximum12
Zeros167
Zeros (%)2.1%
Memory size31.8 KiB
2020-12-01T12:38:02.734683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median5
Q36
95-th percentile9
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.417828137
Coefficient of variation (CV)0.2664797285
Kurtosis6.790687998
Mean5.320585342
Median Absolute Deviation (MAD)0
Skewness0.2185217813
Sum43267
Variance2.010236627
MonotocityNot monotonic
2020-12-01T12:38:02.839058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
5561669.1%
 
6158819.5%
 
94565.6%
 
01672.1%
 
21551.9%
 
7650.8%
 
12360.4%
 
8240.3%
 
1070.1%
 
1160.1%
 
Other values (3)120.1%
 
ValueCountFrequency (%) 
01672.1%
 
150.1%
 
21551.9%
 
33< 0.1%
 
44< 0.1%
 
ValueCountFrequency (%) 
12360.4%
 
1160.1%
 
1070.1%
 
94565.6%
 
8240.3%
 

landslide_trigger_code
Real number (ℝ≥0)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.950073783
Minimum0
Maximum15
Zeros50
Zeros (%)0.6%
Memory size31.8 KiB
2020-12-01T12:38:02.939163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q311
95-th percentile14
Maximum15
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.914941615
Coefficient of variation (CV)0.7071783364
Kurtosis-1.651551014
Mean6.950073783
Median Absolute Deviation (MAD)2
Skewness0.3629842854
Sum56518
Variance24.15665108
MonotocityNot monotonic
2020-12-01T12:38:03.047143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
3388047.7%
 
14148618.3%
 
11132716.3%
 
15166.3%
 
134515.5%
 
81071.3%
 
4740.9%
 
7670.8%
 
12640.8%
 
0500.6%
 
Other values (6)1101.4%
 
ValueCountFrequency (%) 
0500.6%
 
15166.3%
 
280.1%
 
3388047.7%
 
4740.9%
 
ValueCountFrequency (%) 
151< 0.1%
 
14148618.3%
 
134515.5%
 
12640.8%
 
11132716.3%
 

landslide_size_code
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.374692573
Minimum0
Maximum4
Zeros530
Zeros (%)6.5%
Memory size31.8 KiB
2020-12-01T12:38:03.146883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7834934439
Coefficient of variation (CV)0.5699408431
Kurtosis0.6444321841
Mean1.374692573
Median Absolute Deviation (MAD)0
Skewness0.8953620938
Sum11179
Variance0.6138619766
MonotocityNot monotonic
2020-12-01T12:38:03.245825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
1496961.1%
 
2175821.6%
 
38069.9%
 
05306.5%
 
4690.8%
 
ValueCountFrequency (%) 
05306.5%
 
1496961.1%
 
2175821.6%
 
38069.9%
 
4690.8%
 
ValueCountFrequency (%) 
4690.8%
 
38069.9%
 
2175821.6%
 
1496961.1%
 
05306.5%
 

Interactions

2020-12-01T12:37:45.983687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:46.162331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:46.268965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:46.377231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:46.483859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:46.587773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:46.690662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:46.793543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:46.896345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:47.000087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:47.110009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:47.216804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:47.325259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:47.427770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:47.533758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:47.641504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:47.746329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:47.848697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:47.952160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:48.056469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:48.160521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:48.267284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:48.377302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:48.484758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:48.590968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:48.701167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:48.812254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:48.922063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:49.031285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:49.236063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:49.344485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:49.454789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:49.564980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:49.677032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:49.788341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:49.896346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:50.008551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:50.122301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:50.231770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:50.340318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:50.449836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:50.557286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:50.667015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:50.779414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:50.894631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:51.001072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:51.107273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:51.216761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:51.326300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:51.451051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:51.568564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:51.686313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:51.801131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:51.918744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:52.038788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:52.160280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:52.352347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:52.455868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:52.562217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:52.668189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:52.772301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:52.874013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:52.978352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:53.081887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:53.191266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:53.296118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:53.404346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:53.509084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:53.611128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:53.718323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:53.826294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:53.933349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:54.038197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:54.142290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:54.245757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:54.352131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:54.459158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:54.568238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:54.671326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:54.776138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:54.882138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:54.989960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:55.095553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:55.199054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:55.394397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:55.496352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:55.600569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:55.706312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:55.814211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:55.920087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:56.026534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:56.134397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:56.244498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:56.353069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:56.457398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:56.563463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:56.668808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:56.775775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:56.884630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:56.995072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:57.100969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:57.209851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:57.319155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:57.432158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:57.540498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:57.648303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:57.755867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:57.864177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:57.972308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:58.082914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:58.195575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:58.398637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:58.508701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:58.619783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:58.733312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:58.845530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:58.956346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:59.065588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:59.174717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:59.285884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:59.398206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-01T12:38:03.354083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-01T12:38:03.499227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-01T12:38:03.642768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-01T12:38:03.793175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-01T12:38:03.930217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-01T12:37:59.599669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-01T12:37:59.768186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexlandslide_categorylandslide_triggerlandslide_sizefatality_countadmin_division_populationgazeteer_distancelongitudelatitudeyearmonthlandslide_category_codelandslide_trigger_codelandslide_size_code
00landsliderainlarge11.00.041.02145107.450032.5625200885110
11mudslidedownpoursmall0.036619.00.60342-122.663045.420020091632
22landslidedownpourlarge10.014708.00.85548-75.3587-11.129520071530
33landslidemonsoonmedium1.020908.00.7539581.708028.837820097581
44landslidetropical_cyclonemedium0.0798634.02.02204123.897810.33362010105131
55landslidedownpourmedium0.02404.02.28967124.966810.700420122531
66mudslidedownpoursmall0.02126.019.97241-117.266548.279720123632
77complextropical_cyclonemedium3.03191.010.88351-107.622024.9531200790131
89complexdownpourmedium2.00.055.18512100.084523.8900200811031
910complexdownpourmedium4.01023674.06.82298102.695025.0967200811031

Last rows

df_indexlandslide_categorylandslide_triggerlandslide_sizefatality_countadmin_division_populationgazeteer_distancelongitudelatitudeyearmonthlandslide_category_codelandslide_trigger_codelandslide_size_code
81229862rock_fallconstructionmedium0.04038.039.42196-116.436251.4197201610901
81239864landslidedownpourmedium0.01453975.02.11669101.70363.1497201511531
81249865landsliderainmedium6.02000.03.8016491.798626.1699200775111
81259866landslidedownpourmedium2.07630.020.0553067.379930.621820151531
81269871landslidedownpourmedium0.06408.03.58504124.79626.387920111531
81279881landslidedownpourmedium0.031089.01.62917124.73336.366620111531
81289910landslidedownpourmedium0.02689.01.92779125.47827.142620091531
81299932landslidetropical_cyclonemedium0.016671.00.86072125.96677.6000201415131
81309995landslidedownpoursmall0.04534.04.80321-122.954238.473820112532
813110012landslidedownpourmedium0.04534.00.21134-122.995538.503720113531